| Summary: | Poisson regression model has been widely used for modeling claim count data in actuarial and insurance literatures.However, in several cases, claim count data often have excessive number of zeros than are expected in the Poisson model. In that case the Poisson regression may underestimate the standard errors and giving misleading
inference about the regression parameters. This paper aims to apply the zero-inflated regression models on vehicle theft crime data.These zero-inflation phenomenon is a very specific type of over dispersion and zero-inflated Poisson (ZIP) regression model has been suggested for handling zero-inflated data.If the crime count data continue to suggest additional over dispersion, the alternative models the zero-inflated negative binomial-1 (ZINB-1) and the zero-inflated negative binomial-2 (ZINB-2) will be fitted on the private car theft claim count data.In addition, two different forms of link function will be used in the fitting procedure of the zero-inflated regression models, producing different estimates for each model.The results of this study indicate that the ZINB-2 models is better compared to ZIP regression model for handling zero-inflated and additional overdispersed crime count data.
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